We use referential translation machines (RTM) for quality estimation of translation outputs. RTMs are a computational model for identifying the translation acts between any two data sets with respect to interpretants selected in the same domain, which are effective when making monolingual and bilingual similarity judgments. RTMs achieve top performance in automatic, accurate, and language independent prediction of sentence-level and word-level statistical machine translation (SMT) quality. RTMs remove the need to access any SMT system specific information or prior knowledge of the training data or models used when generating the translations and achieve the top performance in WMT13 quality estimation task (QET13). We improve our RTM models with the Parallel FDA5 instance selection model, with additional features for predicting the translation performance, and with improved learning models. We develop RTM models for each WMT14 QET (QET14) subtask, obtain improvements over QET13 results, and rank $1$st in all of the tasks and subtasks of QET14.